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Alzheimer’s disease: a step towards prognosis using smart wearables
Antonella Daniela Pontoriero, Peter Charlton, Jordi Alastruey-Arimon
Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, King’s Health Partners, London, UK

Published: 14 November 2018 by MDPI AG in Proceedings in 5th International Electronic Conference on Sensors and Applications session Wearable Sensors
MDPI AG, Volume 4; 10.3390/ecsa-5-05742
Abstract:

Alzheimer’s disease (AD) is the most common cause of dementia. Several haemodynamic risk factors for AD have been identified, including high systolic blood pressure (BP), brain hypoperfusion and arterial stiffness, as well as ageing. We propose a novel approach for assessing haemodynamic risk factors by analysing arterial pulse waves (PWs). The aim of this feasibility study was to determine whether features extracted from PWs might have utility for stratifying patients at risk of AD.

A numerical model of PW propagation was used to simulate arterial PWs at a range of potential measurement sites, for virtual subjects of each age decade from 25 to 75 years, with subjects at each age exhibiting normal variation in BP and arterial stiffness. Several PW features were extracted, and their relationships with AD risk factors were investigated.

PWs closer to the brain (at carotid and temporal arteries) were found to be suitable for detecting haemodynamic changes associated with AD. Several candidate PW features were identified for future clinical testing. These included features extracted from both BP and photoplethysmogram (PPG) PWs.

This study demonstrates the potential feasibility of using non-invasive PWs to assess haemodynamic risk factors for AD. Not only could these factors be assessed from the BP PW, which is usually measured by a skilled operator, but also from the PPG, which can be acquired by smart watches and phones. If the findings are replicated in clinical studies, then this may provide opportunity for patients to assess their own risk and make lifestyle changes accordingly.

Keywords: Blood Pressure, Photoplethysmogram, Pulse Wave, hypoperfusion, Alzheimer’s
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